• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.5
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• pp.220-230
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• 2017

In this study we apply and compare a variety of numerical methods for calculating residence time distribution in decay tanks, a major design component in the for reducing N-16 radioactivity. Our research group has used a streamlined method using user-defined particle numbers. However, this streamlined method has several problems, including low exiting particle ratios, particle diminishing, and unphysical time distribution, among others. We utilize three numerical methods to establish residence time and time distribution (streamlined, discrete phase method [DPM], and user defined scalar [UDS]) and subsequently compare the averaged results of each. The three tests demonstrate the flow features within the decay tanks, which are then numerically simulated to enable comparison. We conclude that although each simulation predicts similar time averages, the UDS methodology provides a smoother time distribution and tracer contour plots at specific times.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.566-571
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• 2008

In this paper, we propose a footstep planning method of biped robot based on the Particle Swarm Optimization(PSO). We define configuration and locomotion primitives for biped robots in the 2 dimensional workspace. A footstep planning method is designed using learning process of PSO that is initialized with a population of random objects and searches for optima by updating generations. The footstep planner searches for a feasible sequence of locomotion primitives between a starting point and a goal, and generates a path that avoids the obstacles. We design a path optimization algorithm that optimizes the footstep number and planning cost based on the path generated in the PSO learning process. The proposed planning method is verified by simulation examples in cluttered environments.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3197-3202
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• 2007

An analysis program for pedestrian flow has been developed to investigate the flow patterns of passenger in railway stations. Analysis algorithms for pedestrian flow based on DEM(Discrete Element Method) are newly developed. There are lots of similarity between particle-laden two phase flow and passenger flow. The velocity component of 1st phase corresponds to the unit vector of calculation cell, each particle to passenger, volume fraction to population density and the particle velocity to the walking velocity, etc. And, the walking velocity of passenger is also represented by the function of population density. Key algorithms are developed to determine the position of passenger, population density and numbering to each passenger. By using the developed program, we compared the simulation results of the effects of the location and size of exit and elapsed time.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.5
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• pp.882-889
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• 2010

This paper presents a method of finding the optimal operating point minimizing a given objective function with 3 phase power flow equations and operational constraints, called 3 phase optimal power flow (3POPF). 3 phase optimal power flow can provide operation and control strategies for the distribution systems with distributed generation assets, which might be frequently in unbalanced conditions assuming that high penetration rate of renewable energy sources in the systems. As the solution technique for 3POPF, this paper adopts a simulation-based method of particle swarm optimization (PSO). In the PSO based 3POPF, a utility function needs to be defined for evaluation of the degree in operational improvement of each particle's current position. To evaluate the utility function, in this paper, NR-based 3 phase power flow algorithm was developed which can deal with looped distributed generation systems. In this paper, illustrative examples with a 5-bus and a modified IEEE 37-bus test systems are given.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.517-523
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• 2011

The estimation filter in radar systems must track targets' position within low tracking error. In the Multi-Function Radar(MFR), ${\alpha}-{\beta}$ filter and Kalman filter are widely used to track single or multiple targets. However, due to target maneuvering, these filters may not reduce tracking error, therefore, may lost target tracks. In this paper, a target tracking filter based on particle filtering algorithm is proposed for the MFR. The advantage of this method is that it can track targets within low tracking error while targets maneuver and reduce impoverishment of particles by the proposed resampling method. From the simulation results, the improved tracking performance is obtained by the proposed filtering algorithm.

• Transactions on Electrical and Electronic Materials
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• v.18 no.3
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• pp.129-135
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• 2017

This paper introduces a design and a simulation of a hybrid active power filter (HAPF) for harmonics reduction given an ideal supply source. The synchronous reference frame method has been used here to identify the reference currents. The proposed HAPF uses a new artificial- intelligence technique called Particle Swarm Optimization (PSO) for tuning the parameters of a proportional and integral controller called PI-PSO. The PI-PSO controller is used to archive optimality for the DC-link voltage of the HAPF-inverter. The hysteresis non-linear current control method is used in this approach to compare the extracted reference and the actual currents in order to generate the pulse gate required for the HAPF. Results obtained by simulations with Matlab/Simuling show that the proposed approach is very flexible and effective for eliminating harmonic currents generated by the non-linear load with the HAPF based PSO tuning.

• Journal of the Korea Society for Simulation
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• v.28 no.4
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• pp.21-32
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• 2019

Inspired by the social behavior models of a bird flock or fish school, particle swarm optimization (PSO) is a popular metaheuristic optimization algorithm and has been widely used from solving a complex optimization problem to learning a artificial neural network. However, PSO is difficult to apply to many real-life optimization problems involving stochastic noise, since it is originated in a deterministic environment. To resolve this problem, this paper incorporates a resampling method called the uncertainty evaluation (UE) method into PSO. The UE method allows the particles to converge on the accurate optimal solution quickly in a noisy environment by selecting the particles' global best position correctly, one of the significant factors in the performance of PSO. The results of comparative experiments on several benchmark problems demonstrated the improved performance of the propose algorithm compared to the existing studies. In addition, the results of the case study emphasize the necessity of this work. The proposed algorithm is expected to be effectively applied to optimize complex systems through digital twins in the fourth industrial revolution.

• Proceedings of the Korea Society for Simulation Conference
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• 2003.06a
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• pp.161-166
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• 2003

Semiconductor Manufacturing has emerged as one of the most Important world industries. Even with the highly automated and precisely monitored facilities used to process the complex manufacturing steps in a near particle free environment, processing variations in wafer fabrication still exist. The causes of these variations may arise from equipment malfunctions, delicate and difficult processing steps, or human mistakes. In this paper, we could specify the cause stage and the cause equipment and take countermeasures at a speed by the conventional method, without depending on the experience and skills of the engineer

• Journal of the Korea Society for Simulation
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• v.12 no.3
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• pp.13-20
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• 2003

Semiconductor Manufacturing has emerged as one of the most important world industries. Even with the highly automated and precisely monitored facilities used to process the complex manufacturing steps in a near particle free environment, processing variations in wafer fabrication still exist. The causes of these variations may arise from equipment malfunctions, delicate and difficult processing steps, or human mistakes. In this paper, we could specify the cause stage and the cause equipment and take countermeasures at a speed by the conventional method, without depending on the experience and skills of the engineer

• Tribology and Lubricants
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• v.2 no.1
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• pp.39-45
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• 1986

Wear simulation test have been conducted in mild, severe and transient wear regions by a four-ball wear tester. Wear particles are separatively deposited by the RPD technique, and quantitatively analysed in terms of wear particles size distribution. Characteristics of the wear particle size distribution are compare to the results of wear tests through both weibull parameters and center moment method of the weibull distribution function.